Background subtraction segments moving objects from the background in a video acquired of a scene. The segmentation of moving objects can be used to determine trajectories of the objects, and to improve object detection and classification.
Background subtraction can be done by statistical motion flow analysis or an algebraic decomposition. Statistical motion flow methods generally utilize Gaussian mixture models (GMM) of image plane motion to determine the motion of the objects.
Algebraic (QR) decompositions model the background scene as a low dimensional subspace. The moving objects are then segmented as error terms in an orthogonal complement of the background space. When the camera is stationary, the dimensional subspace is low rank and methods, such as robust principle component analysis (RPCA), can successfully segment the foreground from the background. When the camera is moving, the low rank structure no longer holds, and adaptive subspace estimation techniques are used to track, the background subspace.